MCPInstall

deepline-start

Interview a user to discover a concrete GTM problem or manual task, recommend the right installed Deepline skill, prebuilt Play, recipe, or tool, confirm the plan and org, then start the chosen workflow. Use only when the user explicitly invokes deepline-start.

Installs
2.3K
Last 24h
+87
Trending rank
#2178
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://code.deepline.com --skill deepline-start
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads deepline-start automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Discover a Deepline GTM workflow

Quick Start

npm install -g deepline
deepline -h

Use a one-question-at-a-time interview to turn an unclear GTM need into a specific, executable Deepline plan. This skill adapts the grilling interview pattern; it does not require that skill to be installed.

Route first

Start this interview only when the user explicitly invokes deepline-start. Do not invoke it automatically for unclear GTM requests or from another skill.

When the user invokes this skill, keep the discovery and confirmation flow. If the outcome, inputs, and scope are already clear, skip redundant questions and propose the plan for confirmation. For a specific execution request that did not invoke this skill or ask for discovery, use its owning skill directly. deepline-gtm is the generalist for GTM implementation. A specialist leads when its narrower job is the user's actual goal; it may route back to deepline-gtm for execution.

Emerging needLikely ownerWhat to verify before recommending
Find accounts or people, enrich a list, write outreach, or automate row workdeepline-gtmInput identifiers, target criteria, useful output, and batch size
Build a TAM, source a portfolio, map an account, or find qualified rolesdeepline-gtm recipeWhich established recipe matches the target and available identifiers
Watch for new hiring, funding, or other provider eventsdeepline-gtm monitor routeEvent definition, cadence, downstream action, and access
Discover public/private sources, current market language, or provider coveragedeepline-researchResearch question, evidence needed, recency, and source access
Learn ICP signals, audit won/lost evidence, or score accounts/leadsdeepline-scoringDecision, population, labels or approved rules, and evaluation goal
Answer a funnel, pipeline, or RevOps metric questiondeepline-analyticsMetric, period, grain, and governed data source
Build or upload an ads audiencedeepline-ads-audiencesPlatform, audience, suppression, data rights, and destination
Turn a defined state machine into a durable Playdeepline-engineStates, transitions, inputs, and outputs
Review and improve existing Play results with a humandeepline-plays-reviewPlay/run, review surface, and acceptance criteria

These are starting points, not a complete inventory. Inspect the skills available in the current agent environment before naming an owner, and read the candidate's SKILL.md once the job is clear. Read its guidance without invoking its workflow during the interview: an early handoff can replace the one-question interview with the specialist's intake or execution. Do not claim an uninstalled skill is available. If the specialist is unavailable, route through the installed deepline-gtm guidance where it covers the task; otherwise state the gap and give the user a usable plan.

Interview

Ask one concise question per turn, wait for the answer, and give your recommended answer or a short set of choices with a recommendation. Follow the branch the answer opens; do not send a questionnaire. Look up facts available from supplied files, the current environment, or read-only catalog calls instead of asking the user to recall them. Decisions about priorities, acceptable output, data use, spend, and external actions belong to the user. When you cannot recommend the business answer yet, recommend how to decide between the choices rather than guessing the user's preference. Include that recommendation even when asking about files or systems: recommend an input format or inspection approach without inventing the missing fact.

Start with the result they want or the recurring work that consumes time. If they cannot name a problem, offer concrete starting points: building target lists, filling missing contact data, researching accounts or buyer language, ranking accounts, personalizing outreach, preparing ad audiences, and measuring pipeline. Ask which is most costly or blocked today.

For example: “Which recurring task costs your team the most time right now: finding target accounts, filling missing contact data, researching accounts, ranking leads, or preparing outreach? I recommend starting with the one you can show me a recent example of.” Wait for that answer before drilling down.

As the branch becomes clearer, resolve only uncertainties that change the workflow:

  • What happens now, with one recent example and the manual steps?
  • Who or what is processed, and what identifiers or private sources already exist?
  • What decision or deliverable would make the work useful, and how will the user judge success?
  • How many items and how often, and where should the result go?
  • Which constraints matter: geography, freshness, quality, budget, data rights, or permission for outreach, CRM writes, recurring work, and paid calls?

Do not demand every answer before proposing a plan. Mark a nonessential unknown as an assumption or first validation step. Revisit a branch when an answer contradicts the current hypothesis. Briefly summarize what you heard as the problem sharpens, and ask the next highest-impact question.

Select the workflow

Once the job is specific enough, inspect the candidate skill instructions and choose one primary owner and any necessary sequence of supporting skills. For most GTM implementation plans, the primary owner is deepline-gtm. Use deepline-research during planning when uncertainty about public/private sources or market language would materially change that GTM plan; carry its read-only findings into the proposal and defer paid probes until approved. If discovering or evaluating a score is the goal, let deepline-scoring lead and use deepline-gtm for any collection it needs. Do not run a broad research pass when the input and route are already known.

For a prospective Deepline operation, use read-only live catalog search to look for a matching prebuilt Play or tool and describe promising contracts; names in skills are hints, not proof that a capability is available now. Run deepline --maintenance off org status by itself before other Deepline commands in this session. Use --maintenance off for every pre-confirmation CLI check, including catalog search and org status, so automatic updates or skill synchronization do not install anything before approval. Search with the known input and desired result, then inspect a candidate's input, output, and Deepline-facing price. If the CLI or workspace is unavailable, recommend the workflow category and label the exact route and price unverified. Never invent a prebuilt, provider result, coverage, or cost.

Prefer a matching prebuilt Play for repeatable or batch work, a direct tool for an isolated lookup, and a custom Play when transformation, branching, joins, or fallbacks are needed. A specialist recipe is appropriate when its job and inputs match. Use the smallest coherent sequence that reaches the outcome. Name the owning skill even when the work needs no provider calls. For example, drafting outreach from already-enriched rows still uses deepline-gtm and its outreach guidance; a small scope does not remove that handoff.

Confirm and hand off

Present a compact plan with:

  1. The user's problem and the measurable result.
  2. Inputs and constraints, including any assumptions.
  3. The primary skill, any supporting skills, and the proposed Play, recipe, or tool category; name a specific live candidate only when verified.
  4. The first validation step, intended output/destination, and any approval still needed for paid or external actions.
  5. The verified active Deepline org where the workflow will run.

Before presenting that plan, read the primary skill's SKILL.md and check the proposed sequence against it. A description or routing-table entry alone cannot establish the specialist's source order, prerequisites, or quality gates.

Resolve the effective org in the same working directory and command environment that execution will use. Read the Organization and Organization ID lines from that deepline --maintenance off org status result; if the command context has changed, run it again. Show the verified org name with the plan, adding its ID only if needed to distinguish duplicate names. Do not infer the org from the user's company, an input file, remembered credentials, or a global default that folder auth may override. If the CLI, authentication, or org cannot be verified, continue planning but hold execution until the target is resolved.

Ask one explicit confirmation question covering both plan and org, such as “Proceed with this plan in Deepline org <verified org name>?” and wait. Approval of a plan without its org does not authorize the handoff: include the verified org in the combined confirmation even when the plan was already agreed. Check the conversation for the user's explicit acceptance of the displayed org before proceeding. “The plan is confirmed” or “start now” without an accepted org name is not org approval. A CLI result identifies the active org; it does not supply user consent. If org acceptance is absent, ask the combined confirmation and stop; never tell the implementing skill that both were approved. If the user chooses another org, use the documented deepline org set path for that choice, then verify the effective org. Preserve an explicit approval of both the plan and that chosen org instead of asking the same question again. Do not execute paid or state-changing provider calls, create or publish Plays, write to destinations, install skills, or start recurring work before confirmation. Read-only public research and catalog inspection are allowed to make the proposal concrete. If the user changes the plan, revise it and confirm the revised version.

After confirmation, complete these steps in order. This skill owns discovery; the selected skill owns implementation.

  1. Run deepline --maintenance off org status before loading the selected skill. Compare the effective org ID with the confirmed one; an earlier org status result is not a substitute for this check. A mismatch blocks the handoff and execution until resolved with the user. This read-only identity check also applies when the workflow otherwise only creates local files.
  2. Prepare the agreed brief: outcome, population, identifiers/sources, success criteria, constraints, approved scope, destination, selected candidate, and unresolved assumptions, plus the confirmed org name and ID. State that the user confirmed both the plan and org.
  3. Call the skill loader for the primary skill before any implementation tool call. If a Skill tool is available, call it with the selected skill name and the agreed brief as arguments. Reading its file during planning does not replace this call. If no skill loader exists, read its entrypoint and explicitly start following it with the brief.
  4. Under the selected skill, take the first authorized implementation step in this conversation. Do not ask the user to paste the brief, restart discovery, or repeat a confirmation they already gave.

Preserve exact totals, per-item counts, filenames, and action limits; quote the user's approved requirement when paraphrasing could change it. Have the implementing skill check its result against these success criteria before claiming completion. The user should not have to restate the brief or paste a generated prompt. Follow that skill's own pilot, spend, data-rights, and external-action gates; confirmation of a plan does not imply blanket approval for paid calls, outreach, CRM writes, or recurring automation. If an approval was already explicit and bounded, preserve it instead of asking again.

FAQ

What does the deepline-start skill do?

Interview a user to discover a concrete GTM problem or manual task, recommend the right installed Deepline skill, prebuilt Play, recipe, or tool, confirm the plan and org, then start the chosen workflow. Use only when the user explicitly invokes deepline-start.

How do I install deepline-start?

Run npx skills add https://code.deepline.com --skill deepline-start in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/deepline-start/ (personal) or .claude/skills/deepline-start/ (project).

Which AI agents can use deepline-start?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is deepline-start safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains deepline-start?

It is published by code.deepline.com in code.deepline.com. It has 2,341 recorded installs.

More from code.deepline.com

View all →

niche-signal-discovery

code.deepline.com

Deprecated alias. This skill was renamed to deepline-scoring. Use when a user or script still invokes /niche-signal-discovery: immediately load and follow the deepline-scoring skill instead. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals.

Docs & Writing36.4K installs

build-tam

code.deepline.com

Build a Total Addressable Market list: size the audience, source companies from ICP filters, then find contacts at those companies at scale.

36.4K installs

portfolio-prospecting

code.deepline.com

Find companies backed by a specific investor or accelerator, then find contacts and build personalized outbound.

Business & Finance36.3K installs

deepline-feedback

code.deepline.com

Retired. Reporting feedback and sharing a session are documented by the CLI itself: run deepline feedback send --help and deepline sessions send --help. Kept only so installed CLIs that still list this skill keep syncing.

36.3K installs

clay-to-deepline

code.deepline.com

Convert a Clay table, or a whole Clay workspace via the Clay CLI, into Deepline plays. Handles extraction, documentation, action mapping, play generation, and cell-level parity validation against Clay ground truth.

Docs & Writing36.3K installs

linkedin-url-lookup

code.deepline.com

Retired. LinkedIn URL lookup is part of deepline-gtm. Kept only so installed CLIs that still list this skill keep syncing.

Marketing & SEO36.2K installs

Popular productivity & automation skills

View category →

agent-browser

vercel-labs/agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

OfficialProductivity & Automation1.3M installs

lark-doc

open.feishu.cn

飞书云文档(Docx / Wiki)内容操作:读取、创建、编辑文档,插入或下载图片附件,以及操作思维笔记。用户提供文档 URL/token(包括 doubao.com 的 /docx/、/wiki/)时使用;按 URL 路径/token 而非域名路由;/page/ 是妙搭应用,非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive;随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。

lark-base

open.feishu.cn

飞书多维表格(Base)操作:建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式(BaseApp/AppMode 页面与组件)、Workspace 目录、workflow、角色权限、模板中心(多维表格模板分类/列表/搜索);遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps;文件导入/导出转 lark-drive,认证/授权转 lark-shared。

lark-drive

open.feishu.cn

飞书云空间(云盘/云存储):管理 Drive 文件和文件夹,包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签(secure labels)和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题,或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用;doubao.com 云空间 URL/token 也按资源路径和 token 路由,不回退 WebFetch。不负责:文档内容编辑(走 lark-doc)、表格/Base 表内数据操作(走 lark-sheets/lark-base)、知识空间节点/成员管理(走 lark-wiki)、原生 Markdown 文件读写/patch/diff(走 lark-markdown)。

lark-shared

open.feishu.cn

Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

lark-im

open.feishu.cn

飞书即时通讯:收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片(Interactive Card)、监听卡片按钮回调(card.action.trigger)。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶(添加/移除/查询置顶会话)、管理标签数据、处理卡片回调时使用。